Virtual sensing for adjoint based incorporation of supplementary data sources in inversion

ABSTRACT

A method, system and computer program product for integrating plural modalities of information to obtain values for a specified attribute of a given system. In one embodiment, data of a first modality, conveying a first source of data of a first type of the system is acquired; a simulator is configured with settings of physical sensors; and data of a second modality from the system, conveying a second source of data of a second type of the system is acquired. The data of the second modality is converted to data of the first type, while configuring a virtual set of sensors to enable acquisition of the converted data of the second modality; and an adjoints equipped simulator is configured with settings of the virtual sensors, to mimic collection of data of the first type, while configured to measure data of the second type.

BACKGROUND

This invention generally relates to integration of plural sources ofinformation in inversion, and more specifically, to incorporation ofplural sources of data of different modalities in inversion. Anembodiment of the invention relates to combining such plural sources ofdata using adjoint based optimization methods.

In many situations, it is essential to be able to predict how the stateand the attributes of a system vary over time. For example, predictionof flow of fluids through a reservoir for certain scenarios, e.g. wellplacement, production optimization, etc., is desirable for supportingbusiness/investment decisions. For efficient recovery of oil and gasfrom a reservoir, a good understanding of the subsurface attributes andits constitutes is vital.

Conventionally, production data comprised of measurements of pressuresin the wells, along with fluid (oil and water) and gas flow rates, isused in attempt to recover the subsurface attributes. The process inwhich this is performed is called history matching. In this process, themodel parameters (such as permeability, porosity, skin, seal factors)are being adjusted so that simulation of flow would match the recordedproduction data at the wells. There are several strategies for updatingthe model parameters, including manual trial and error. The most widelyaccepted approach is based upon non-linear optimization. In thisapproach, the problem is cast as minimization of an objective functionthat comprises a measure of the misfit (likelihood) between the actualmeasured data and the one that is simulated based upon a choice of modelparameters. e.g.:

$\begin{matrix}{{\hat{m} = {\underset{m}{\arg\;\min}\underset{\underset{{data}\mspace{14mu}{misfit}}{︸}}{\left( {{V\left( {u\left( {m;y} \right)} \right)},{d(y)}} \right)}}}{{s.t.\mspace{14mu}{g\left( {m,{u;y}} \right)}} = 0}{constraints}} & \;\end{matrix}$where m denotes the model parameters,

stands for a noise/distance model, V is a function that converts thestate u (saturation and pressure for flow in porous medium) intosimulated measurement, y denotes the experimental design setup and ddenotes the real data. As a constraint, the state u must comply with thegoverning physics of the problem (e.g. flow in porous medium representedby a set of partial differential equations along with appropriateboundary conditions) as represented by the operator g. This objectivefunction may involve additional terms, such as regularization, oradditional constraints (e.g. positivity or bounds for some parameters).

Among the various computational methods to solve this optimizationproblem, adjoint (sensitivity) based methods are acknowledged in theoptimization community as superior, and for large-scale problems, oftenthese approaches are the only computationally tractable resort.

Unfortunately, the acquired production data do not convey sufficientinformation for a complete and stable recovery of the subsurfaceattributes.

BRIEF SUMMARY

Embodiments of the invention provide a method, system and computerprogram product for integrating plural modalities of information, in aninversion process using a defined set of modeling parameters, to obtainvalues for a specified attribute of a given system. In one embodiment,the method comprises acquiring data of a first modality from a set ofphysical sensors, conveying a first source of data of a first type ofthe system; configuring adjoints equipped simulator with settings ofphysical sensors; acquiring data of a second modality from the system,conveying a second source of data of a second type of the system; andconverting the data of the second modality to data of the first type,while configuring a virtual set of sensors to enable acquisition of theconverted data of the second modality. The method further comprisesconfiguring adjoints equipped simulator with settings of the virtualsensors, to mimic collection of data of the first type, while configuredto measure data of second type; computing sensitivities for physical andvirtual sensors using the existing adjoint functionality of thesimulator; and combining the data of the first modality and theconverted data of the second modality together with the sensitivities ofthe physical and virtual sensors, in an inversion process, to obtainvalues for a specified attribute of the given system.

In an embodiment, the data of the first modality and the converted dataof the second modality are assimilated in the inversion process in aspatially and temporally consistent manner.

In one embodiment, the physical sensors have a defined configuration andare used in a defined timing schedule to obtain the first data; and theconverting the data of the second modality includes converting the dataof the second modality to the data of the same type as of the first typeof data, as measured by the virtual sensors in said specifiedconfiguration and in accordance with said defined timing schedule tomaintain spatial and temporal consistency between the first data and theconverted second data.

In one embodiment, the inversion process applies respective weightsbetween the contributions of the physical sensors and the virtualsensors to achieve a specified objective.

In an embodiment, the respective weights are determined by a specifiedmeasure of confidence associated with each of the sources of data.

In an embodiment, the data of the second type includes physicalmeasurements, and the virtual measurements mimic the physicalmeasurements through the use of a weighted virtual measurement.

In one embodiment, the virtual measurements are spatially and temporallyconsistent with the origin of the information, and the virtualmeasurements mimic the physical measurements through the use of aweighted virtual measurement along the path of the trace.

In an embodiment, the system is a reservoir including oil and water.

In one embodiment, the acquiring data of a first modality includesobtaining physical measurements along a plurality of wells in thereservoir; the acquiring data of a second modality includes obtainingsaturation measurements of a plurality of seismic traces along aplurality of virtual wells in the reservoir; and the virtual wells mimicthe saturation measurement of the seismic traces through the use of aweighted virtual measurement along the paths of the traces.

In an embodiment, a virtual sensor comprises of a multitude of gridblocks, and each of said grid blocks have a grid block flow rate; and inorder to match a ratio of volumetric samples from all grid blocks alongthe path of the virtual sensor to corresponding weight factors, the gridblock flow rates correspond to said ratio.

The most advanced simulators offer efficient adjoint-based sensitivitycomputation of the model parameters with respect to a single source ofdata (e.g. reservoir simulator would offer sensitivities that linkbetween a small change in the subsurface parameters and a respectivechange in the well data expected). In embodiments of the invention, inorder to obtain sensitivities with respect to an additional modality(e.g. seismic), interpreted entities (e.g. interpreted saturation forseismic) are integrated through the use of a weighted virtualmeasurement that consistently retains the locality of the information ofadditional modalities (e.g. for seismic attributes) along the path ofthe trace.

The virtual sensor mimics a conventional sensor (e.g. production well),and therefore, the present adjoint computation capability of a simulatorcan be leveraged and extended towards other types and sources of data.In embodiments of the invention, in order to minimize interference ofvirtual sensing upon the original dynamic behavior of the system underconsideration (e.g. influence upon flow and fluid contents in thecontext of flow in porous medium), precautions are taken (e.g. limitvirtual samples quality, etc).

Information is assimilated in a spatially and temporally consistentmanner. The original information (saturation information in the seismiccase) is of a given dimension (e.g. a curve in 3 dimensional space), andrespectively the virtual sensors are positioned along the sametrajectory, thereby avoiding both displacement and interpolation errors.Also, since the virtual sensors are only activated with accordance tothe timing in which the original information was attained, the temporalassimilation is free of interpolation.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 depicts an embodiment of the invention.

FIG. 2 is an illustration of a reservoir perforated by a collection ofvirtual wells.

FIG. 3 illustrates a typical interpreted saturation of a seismic trace.

FIG. 4 shows illustrations of recovered saturation in a reservoir usingonly conventional sensors at three different time instances.

FIG. 5 shows the observed (interpreted) saturation field in thereservoir shown in FIG. 4 using 4D seismic data.

FIG. 6 illustrates saturation maps showing updates of the modelparameters using combined production and seismic data, based onsensitivity information from virtual sensors.

FIG. 7 shows a computing environment that may be used in the practice ofthis invention.

DETAILED DESCRIPTION

This invention relates to integration of plural sources of data ofdifferent modalities in an inversion process to obtain values for one ormore specified parameters of a given system. With reference to FIG. 1,in one embodiment, the invention provides a method comprising acquiring,at 12, data of a first modality from a set of physical sensors 14,conveying a first source of data of a first type of the system;configuring adjoints equipped simulator with settings of physicalsensors; acquiring, at 16, data of a second modality from the system,conveying a second source of data of a second type of the system; andconverting 20 the data of the second modality to data of the first type,while configuring a virtual set of sensors 22 to enable acquisition ofthe converted data of the second modality. The method further comprisesconfiguring adjoints equipped simulator with settings of the virtualsensors, to mimic collection of data of the first type, while configuredto measure data of second type; computing sensitivities for physical andvirtual sensors using the existing adjoint functionality of thesimulator; and combining the data of the first modality and theconverted data of the second modality together with the sensitivities ofthe physical and virtual sensors, in an inversion process 24, to obtainvalues for a specified attribute of the given system.

In embodiments of the invention, data of plural or multiple modalitiesmay be acquired 26 from plural or multiple sources, with each type ofdata converted 30 into attributes of data of the type of the firstsource of data, while configuring an associated virtual set of sensors32, and combining this converted data also in the inversion process 24.

Generally speaking, an inverse problem is a problem in which modelparameters are derived from known data. Inverse problems are generallydifficult to solve for a variety of reasons and are considerably harderfor process-based approaches in sedimentary systems because of thescarcity of field data needed to constrain the process.

The term “adjoint model” refers to a mathematical evaluation of thesensitivity of a predictive model such as a process-based model.Moreover, an adjoint model provides sensitivity relations that representthe extent to which the output of a predictive model varies as its inputvaries. An adjoint model may comprise of computation of the gradient orsensitivity of the acceptance criteria with respect to model parametersby solving an auxiliary set of equations, known as adjoint equations.The adjoint model is an efficient method for computing sensitivities oflarge-scale conditioning tasks and, unlike most methods, thecomputational cost does not scale with the number of conditioningparameters. Many types of adjoint models are known.

This invention may be used in a wide range of specific circumstances. Asone example, embodiments of the invention may be used in the predictionof the flow of fluids through a reservoir. As mentioned above,prediction of flow of fluids through a reservoir for certain scenarios,e.g. well placement, production optimization, etc., is desirable forsupporting business/investment decisions. For efficient recovery of oiland gas from a reservoir, a good understanding of the subsurfaceattributes and its constitutes is vital.

Conventionally, production data comprised of measurements of pressuresin the wells, along with liquid (oil and water) and gas flow rates, isused in attempt to recover the subsurface attributes. The process inwhich this is performed is called history matching. In this process, themodel parameters (such as permeability, porosity, skin, seal factors)are being altered so that simulation of flow would match the recordedproduction data at the wells. There are several strategies for updatingthe model parameters, including manual trial and error. The most widelyaccepted approach is based upon non-linear optimization. In thisapproach, the problem is cast as minimization of an objective functionthat comprises a measure of the misfit (likelihood) between the actualmeasured data and the data that are simulated for a choice of modelparameters. e.g.:

$\begin{matrix}{{\hat{m} = {\underset{m}{\arg\;\min}\underset{\underset{{data}\mspace{14mu}{misfit}}{︸}}{\left( {{V\left( {u\left( {m;y} \right)} \right)},{d(y)}} \right)}}}{{s.t.\mspace{14mu}{g\left( {m,{u;y}} \right)}} = 0}{constraints}} & \;\end{matrix}$where m denotes the model parameters,

stands for a noise/distance model, V is a function that converts thestate u (saturation and pressure for flow in porous medium) intosimulated measurement, y denotes the experimental design setup and ddenotes the real data. As a constraint, the state u must comply with thegoverning physics of the problem (e.g. flow in porous medium representedthrough a set of partial differential equations along with appropriateboundary conditions) as represented by the operator g. This objectivemay involve additional terms, such as regularization, or additionalconstraints (e.g. positivity or bounds for some parameters).

Among the various computational methods to solve this optimizationproblem, adjoint (sensitivity) based methods are acknowledged in theoptimization community as superior and for large-scale problems, oftenthese approaches are the only computationally tractable resort.

Unfortunately, the acquired production data do not convey sufficientinformation for a complete and stable recovery of the subsurfaceattributes. Consequently, the resulted solutions are corrupted byintrusively large null space of the solution space. An intuition forthat concern, is that the sensitivity of the acquired data at the sensorlocations (e.g. wells) towards changes in the model parameters away fromthe sensors is negligibly small. In mathematical terms, theaforementioned limitation is formally referred to as ill-posednessnature of the history-matching problem.

One of the most effective remedies for ill-posedness is throughsupplementation of complementary information from additionalmodality(ies), i.e. from different sources. For instance, in contrast toproduction data, seismic data for instance, in which sound waves areused to estimate the characteristics of the main geological features ofthe reservoir, provide excellent spatial information regarding thesubsurface, yet, this data suffers from very poor temporal resolution.Incorporation of areal data may supplement the poor spatial resolutionof production data and diminish ill-posedness.

Incorporation of multi-modality data (e.g. production andseismic/electromagnetic/gravity/well bore holes measurements) in theprocess of inversion is intricate and delicate. Issues such as therelative weight of the information from each source, coupling ofparameters of various physical entities, alignment of the grids, andefficient derivation of sensitivities, have all introduced great hurdlesalong the way. Handling the above consistently and without requiringmajor re-coding effort was hitherto not possible. In particular, theability to derive efficiently adjoints (sensitivities) of the data withrespect to the model parameters is key for updating model parametersusing gradient-based optimization techniques. The functionality allowsto assess the value of information and assist in decisions such as whenshould additional survey be performed? What should be the spatialresolution of such survey? What should be the granularity of the data?etc.

Several methods have been proposed in attempt to combine additionalsources of data in dynamic history matching:

With Streamline, only sensitivities with respect to permeability can bedetermined efficiently. Derivation of streamlines sensitivities withrespect to additional modalities is often not possible, or at leastrequires a major reformulation and coding effort. Also, as a reducedphysics approach, it involves intrinsic modeling errors and is limitedto specific approximation assumptions.

With Ensemble Kalman Filter methods, updates are restricted to the spacespanned by the ensemble members. In addition, updates of modelparameters may be insensitive to observations, and the number ofrequired simulations is generally large.

With Petrophysics based, Coupling relies on empirical links, andderivation of adjoints are far from trivial.

Additional common drawback to all the above methods is the need ofinterpolation of the additional modalities (e.g. seismic attributes)onto the reservoir grid. Such interpolation incurs additional errorscreeping into the solution.

Modern advanced reservoir simulators offer efficient adjoint-basedsensitivity computation of the model parameters with respect to welldata. In embodiments of the invention, in order to obtain sensitivitieswith respect to an additional modality (e.g. seismic), interpretedentities (e.g. interpreted saturation for seismic) are integratedthrough the use of a weighted virtual measurement along the path of theadditional sources of data (e.g. seismic trace).

The virtual sensor mimics a conventional sensor (e.g. production well),and therefore, the present adjoint computation capability of a simulatorcan be leveraged and extended towards other types of data. Inembodiments of the invention, in order to minimize interference ofvirtual sensing upon the original dynamic behavior of the system underconsideration (influence upon flow and fluid contents in the context offlow in porous medium), precautions are taken. Information isassimilated in a spatially and temporally consistent manner.

The original information (e.g. saturation information in the seismiccase) is of a given dimension (e.g. curves in 3D space), andrespectively the virtual sensors are positioned along the sametrajectory, thereby avoiding both displacement and interpolation errors.Also, since the virtual sensors are only activated with accordance tothe timing in which the original information was attained, the temporalassimilation is free of interpolation.

For example, FIG. 2 illustrates a typical interpreted saturation of aseismic trace. Areas 42 correspond to oil saturation, and areas 44correspond to water, the traces go vertically. Following the curvedtrajectory of each trace, a specific producer well (virtual sensor) issimulated to mimic the saturation information contained in the trace, asif that information was measured by n actual producer wells. FIG. 3 isan illustration of a reservoir (area 50) perforated by a collection ofvirtual wells (area 52, depicts tubes probing through the reservoirsurface).

The virtual sensors can be deployed in any direction, or trajectory,including horizontal, as long as they are consistent with theinformation given for the desired source of information (here, seismicattributes in the form of saturation). In many situations, theinformation is given along curves in 3 dimensional space. In thatregard, placing a tubular virtual well along that trajectory is the mostconsistent way of assimilating such information, avoiding incorporationof unnecessary, and numerically contaminating, interpolation errors.

The virtual wells (area 52) mimic the (interpreted) saturationmeasurement of a seismic trace through the use of a weighted virtualmeasurement along the path of the trace. In order to match the ratio ofvolumetric samples from all grid blocks along the path of the virtualsensor (trace) to the corresponding weight factors, the grid block flowrates correspond to that ratio.

Some simulators do not offer direct prescription of grid block flowrate, in which case, these can still be adjusted indirectly by alteringa multiplicative parameter called individual permeability height productmultipliers.

Since employment of multiple virtual sensors may be desired, and as thesensors are virtual, one necessary demand, in embodiments of theinvention, is that their influence upon the original dynamics would benegligible. In the context of flow in porous medium, precautions tomarginalize influence upon flow and fluid contents may be taken.

Once the virtual sensors are configured properly, an adjoint basedhistory matching process (equipped with virtual sensors) can beperformed. The respective weight between the contribution ofconventional sensors vs. virtual sensors, can be determined by somemeasure of confidence (covariances, signal to noise ratio, etc.)associated with each source of data.

FIG. 4 shows illustrations of recovered saturation in a reservoir usingonly conventional sensors at three different time instances, 1998, 2001,and 2004.

As a comparison, the observed saturation using 4D seismic is presentedin FIG. 5.

The two sets display large errors between the saturation recovered usingconventional sensors and the observed saturation data obtained through4D seismic.

Updates of the model parameters using combined production and seismicdata, based on the sensitivity information from the virtual sensors,resulted in the saturation maps presented in FIG. 6.

The updated model of FIG. 6 shows a substantial improvement in thesaturation data mismatch. Besides the improved model performance,virtual sensing workflow is highly efficient. The principles of virtualsensing are generic, and can be used for consistent and highly efficientincorporation of multiple data sources and types, other than the twodemonstrated here (production data with seismic attributes).

Referring to FIG. 7, the present invention may be a system, a method,and/or a computer program product. The computer program product mayinclude a computer readable storage medium (or media) having computerreadable program instructions thereon for causing a processor to carryout aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

A computer-based system 100 in which embodiments of the invention may becarried out is depicted in FIG. 7. The computer-based system 100includes a processing unit 110, which houses a processor, memory andother system components (not shown expressly in the drawing) thatimplement a general purpose processing system, or a computer that mayexecute a computer program product. The computer program product maycomprise media, for example a compact storage medium such as a compactdisc, which may be read by the processing unit 110 through a disc drive120, or by any means known to the skilled artisan for providing thecomputer program product to the general purpose processing system forexecution thereby.

The computer program product may comprise all the respective featuresenabling the implementation of the inventive method described herein,and which—when loaded in a computer system—is able to carry out themethod. Computer program, software program, program, or software, in thepresent context means any expression, in any language, code or notation,of a set of instructions intended to cause a system having aninformation processing capability to perform a particular functioneither directly or after either or both of the following: (a) conversionto another language, code or notation; and/or (b) reproduction in adifferent material form.

The computer program product may be stored on hard disk drives withinprocessing unit 110, as mentioned, or may be located on a remote systemsuch as a server 130, coupled to processing unit 110, via a networkinterface such as an Ethernet interface. Monitor 140, mouse 150 andkeyboard 160 are coupled to the processing unit 110, to provide userinteraction. Printer 170 is provided for document input and output.Printer 170 is shown coupled to the processing unit 110 via a networkconnection, but may be coupled directly to the processing unit. Scanner180 is shown coupled to the processing unit 110 directly, but it shouldbe understood that peripherals might be network coupled, or directcoupled without affecting the performance of the processing unit 110.

While it is apparent that embodiments of the invention herein disclosedare well calculated to fulfill the features discussed above, it will beappreciated that numerous modifications and embodiments may be devisedby those skilled in the art, and it is intended that the appended claimscover all such modifications and embodiments as fall within the truespirit and scope of the present invention.

The invention claimed is:
 1. A computer-implemented method ofintegrating plural modalities of information, in an inversion processusing a defined set of modeling parameters, to obtain values for aspecified attribute of a given physical system, the method comprising:acquiring data of a first modality from a set of physical sensors in thegiven physical system; acquiring data of a second modality from thegiven physical system integrating the data of the first modality and thedata of the second modality through the use of virtual measurements anda simulator, including configuring a computer system as a simulator,with adjoint functionality, and with settings of the physical sensors,configuring the simulator with settings of a set of virtual sensors inthe physical system to mimic the physical sensors, as if the data of thesecond modality were measured by the physical sensors of the set ofphysical sensors, to convert the data of the second modality to data ofthe first modality, and positioning the virtual sensors in the givenphysical system to maintain spatial consistency with the physicalsensors, and activating the virtual sensors in accordance with a giventiming to maintain temporal consistency with the physical sensors;computing, by the computer system, sensitivities for the physicalsensors and the virtual sensors using the adjoint functionality of thesimulator; and combining, by the computer system, the data of the firstmodality of the first type of data and the data of the second modalityconverted to data of the first type, together with the sensitivities ofthe physical sensors and the virtual sensors, in an inversion process,to obtain values for a specified attribute of the given physical system.2. The method according to claim 1, wherein: the physical sensors have adefined configuration and are used in a defined timing schedule toobtain the first data; and the converting the data of the secondmodality includes converting the data of the second modality to the dataof the same type as of the first type of data, as measured by thevirtual sensors in said specified configuration and in accordance withsaid defined timing schedule to maintain spatial and temporalconsistency between the first data and the converted second data.
 3. Themethod according to claim 2, wherein the using the inversion processincludes applying respective weights between the contributions of thephysical sensors and the virtual sensors to achieve a specifiedobjective.
 4. The method according to claim 3, wherein the respectiveweights are determined by a specified measure of confidence associatedwith each of the sources of data.
 5. The method according to claim 1,wherein: the data of the second types includes specified physicalmeasurements obtained from the given physical system; and the virtualmeasurements mimic the physical measurements through the use of aweighted virtual measurement.
 6. The method according to claim 5,wherein: the virtual measurements are spatially and temporallyconsistent with the origin of the information; and the virtualmeasurements mimic the physical measurements through the use of aweighted virtual measurement along the path of the trace.
 7. The methodaccording to claim 1, wherein the system is a reservoir including oiland water.
 8. The method according to claim 7, wherein: the acquiringdata of a first modality includes obtaining specified physicalmeasurements along a plurality of wells in the reservoir; the acquiringdata of a second modality includes obtaining saturation measurements ofa plurality of seismic traces along a plurality of virtual wells in thereservoir; and the virtual wells mimic the saturation measurement of theseismic traces through the use of a weighted virtual measurement alongthe paths of the traces.
 9. The method according to claim 8, wherein: avirtual sensor comprises of a multitude of grid blocks, and each of saidgrid blocks have a grid block flow rate; and in order to match a ratioof volumetric samples from all grid blocks along the path of the virtualsensor to corresponding weight factors, the grid block flow ratescorrespond to said ratio.
 10. The method according to claim 1, wherein:the acquiring data of a second modality from the given physical systemincludes acquiring the data of the second modality along a specifiedtrace in the given physical system; and the positioning the virtualsensors in the given physical system includes positioning the virtualsensors along the specified trace in the given physical system therebymaintaining the spatial consistency with the physical sensors.
 11. Acomputer processing system for integrating plural sources of informationin an inversion process to obtain values for one or more specifiedparameters of a defined physical system, the plural sources ofinformation including data of a first type from a first source of dataand data of a second type from a second source of data, the computerprocessing system comprising: one or more processing units configuredfor: acquiring data of a first modality from a set of physical sensorsin the given physical system; acquiring data of a second modality fromthe given physical system; integrating the data of the first modalityand the data of the second modality through the use of virtualmeasurements and a simulator, including configuring a computer system asa simulator, with adjoint functionality, and with settings of thephysical sensors, configuring the simulator with settings of a set ofvirtual sensors in the physical system to mimic the physical sensors, asif the data of the second modality were measured by the physical sensorsof the set of physical sensors, to convert the data of the secondmodality to data of the first modality, and positioning the virtualsensors in the given physical system to maintain spatial consistencywith the physical sensors, and activating the virtual sensors inaccordance with a given timing to maintain temporal consistency with thephysical sensors; computing sensitivities for the physical sensors andthe virtual sensors using the adjoint functionality of the simulator;and combining the data of the first modality of the first type and theconverted data of the second modality converted to the first type,together with the sensitivities of the physical sensors and the virtualsensors, in an inversion process, to obtain values for a specifiedattribute of the given physical system.
 12. The system according toclaim 11, wherein the data of the first modality and the converted dataof the second modality are assimilated in the inversion process in aspatially and temporally consistent manner.
 13. The system according toclaim 11, wherein: the physical sensors have a defined configuration andare used in a defined timing schedule to obtain the first data; and theconverted data of the second modality includes converting the data ofthe second modality to the data of the same type as of the first type ofdata, as measured by the virtual sensors in said specified configurationand in accordance with said defined timing schedule to maintain spatialand temporal consistency between the first data and the converted seconddata.
 14. The system according to claim 13, wherein: the use of theinversion process includes applying respective weights between thecontributions of the physical sensors and the virtual sensors to achievea specified objective; and the respective weights are determined by aspecified measure of confidence associated with each of the sources ofdata.
 15. The system according to claim 11, wherein: the data of thesecond type includes specified physical measurements obtained from thegiven physical system; the virtual measurements are taken along a pathof a specified trace in the given system; and the virtual measurementsmimic the physical measurements through the use of a weighted virtualmeasurement that is spatially and temporally consistent with the originof the data.
 16. An article of manufacture comprising: at least onetangible computer readable hardware device having computer readableprogram code logic tangibly embodied therein to integrate plural sourcesof information in an inversion process to obtain values for specifiedparameters of a given physical system, the computer readable programcode logic, when executing on a computer, performing the following:acquiring data of a first modality from a set of physical sensors in thegiven physical system; acquiring data of a second modality from thegiven physical system; integrating the data of the first modality andthe data of the second modality through the use of virtual measurementsand a simulator, including configuring a computer system as a simulator,with adjoint functionality, and with settings of the physical sensors,configuring the simulator with settings of a set of virtual sensors inthe physical system the physical sensors, as if the data of the secondmodality were measured by the physical sensors of the set of physicalsensors, to convert the data of the second modality to data of the firstmodality, and positioning the virtual sensors to maintain spatialconsistency with the physical sensors, and activating the virtualsensors in accordance with a given timing to maintain temporalconsistency with the physical sensors; computing, by the computersystem, sensitivities for the physical sensors and the virtual sensorsusing the adjoint functionality of the simulator; and combining, by thecomputer system, the data of the first modality of the first type andthe converted data of the second modality converted to the first type,together with the sensitivities of the physical sensors and the virtualsensors, in an inversion process, to obtain values for a specifiedattribute of the given physical system.
 17. The article of manufactureaccording to claim 16, wherein the data of the first modality and theconverted data of the second modality are assimilated in the inversionprocess in a spatially and temporally consistent manner.
 18. The articleof manufacture according to claim 16, wherein: the physical sensors havea defined configuration and are used in a defined timing schedule toobtain the first data; and the converted data of the second modalityincludes converting the data of the second modality to the data of thesame type as of the first type of data, as measured by the virtualsensors in said specified configuration and in accordance with saiddefined timing schedule to maintain spatial and temporal consistencybetween the first data and the converted second data.
 19. The article ofmanufacture according to claim 16, wherein the given system is areservoir including oil and water, and wherein: the receiving the firstdata includes receiving specified physical measurements along aplurality of wells in the reservoir; the receiving the second dataincludes receiving saturation measurements of a plurality of seismictraces along a plurality of virtual wells in the reservoir; and thevirtual wells mimic the saturation measurement of the seismic tracesthrough the use of a weighted virtual measurement that are temporallyand spatially consistent with the origin of the information.
 20. Thearticle of manufacture according to claim 19, wherein a multitude ofgrid blocks are located in the virtual wells, and each of said gridblocks have a grid block flow rate, and wherein: in order to match aratio of volumetric samples from all grid blocks along the path of thevirtual sensor to corresponding weight factors, the grid block flowrates correspond to said ratio.